A simulated dataset from a fictional study evaluating a special training program by Lieutenant Columbo for detectives. This dataset is specifically designed to demonstrate the handling of missing data. Missing values were introduced into the post-intervention outcome using a Missing At Random (MAR) mechanism.
Format
A tibble with 160 rows and 7 variables:
- detective_id
An integer representing the unique identifier for each detective.
- group
A factor indicating the training group (
"Columbo's Training"or"Control").- age
An integer representing the detective's age.
- gender
A factor for the detective's gender (
"m","f", or"d").- job_frustration
A numeric score from 0-10 indicating job frustration.
- time
A factor for the measurement occasion (
"pre"or"post").- clearance
A numeric value for the outcome, the Case Clearance Rate (in %). This variable contains
NAvalues.
Source
Simulated data where missing values in the post-intervention outcome were
introduced via a Missing At Random (MAR) mechanism. The probability of
missingness depends on observed variables (higher job_frustration and
lower post-intervention scores increase the likelihood of data being missing).